Abstract
Automatic fact verification has become an increasingly popular topic in recent years and among datasets the Fact Extraction and VERification (FEVER) dataset is one of the most popular. In this work we present BEVERS, a tuned baseline system for the FEVER dataset. Our pipeline uses standard approaches for document retrieval, sentence selection, and final claim classification, however, we spend considerable effort ensuring optimal performance for each component. The results are that BEVERS achieves the highest FEVER score and label accuracy among all systems, published or unpublished. We also apply this pipeline to another fact verification dataset, Scifact, and achieve the highest label accuracy among all systems on that dataset as well. We also make our full code available.- Anthology ID:
- 2023.fever-1.6
- Volume:
- Proceedings of the Sixth Fact Extraction and VERification Workshop (FEVER)
- Month:
- May
- Year:
- 2023
- Address:
- Dubrovnik, Croatia
- Editors:
- Mubashara Akhtar, Rami Aly, Christos Christodoulopoulos, Oana Cocarascu, Zhijiang Guo, Arpit Mittal, Michael Schlichtkrull, James Thorne, Andreas Vlachos
- Venue:
- FEVER
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 58–65
- Language:
- URL:
- https://aclanthology.org/2023.fever-1.6
- DOI:
- 10.18653/v1/2023.fever-1.6
- Cite (ACL):
- Mitchell DeHaven and Stephen Scott. 2023. BEVERS: A General, Simple, and Performant Framework for Automatic Fact Verification. In Proceedings of the Sixth Fact Extraction and VERification Workshop (FEVER), pages 58–65, Dubrovnik, Croatia. Association for Computational Linguistics.
- Cite (Informal):
- BEVERS: A General, Simple, and Performant Framework for Automatic Fact Verification (DeHaven & Scott, FEVER 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2023.fever-1.6.pdf